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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-medical-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-medical-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1905
- Precision: 0.6552
- Recall: 0.6965
- F1: 0.6752
- Accuracy: 0.7449
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 71 | 1.8255 | 0.3427 | 0.4460 | 0.3876 | 0.5555 |
| No log | 2.0 | 142 | 1.3139 | 0.4722 | 0.5703 | 0.5166 | 0.6442 |
| No log | 3.0 | 213 | 1.1147 | 0.5258 | 0.6029 | 0.5617 | 0.6886 |
| No log | 4.0 | 284 | 0.9873 | 0.5785 | 0.6151 | 0.5962 | 0.7048 |
| No log | 5.0 | 355 | 0.9282 | 0.6314 | 0.6558 | 0.6434 | 0.7312 |
| No log | 6.0 | 426 | 0.8760 | 0.642 | 0.6538 | 0.6478 | 0.7329 |
| No log | 7.0 | 497 | 0.8501 | 0.6608 | 0.6904 | 0.6753 | 0.7466 |
| 1.1706 | 8.0 | 568 | 0.8313 | 0.6791 | 0.7067 | 0.6926 | 0.7483 |
| 1.1706 | 9.0 | 639 | 0.8002 | 0.6616 | 0.7047 | 0.6824 | 0.7449 |
| 1.1706 | 10.0 | 710 | 0.8280 | 0.6640 | 0.6721 | 0.6680 | 0.7363 |
| 1.1706 | 11.0 | 781 | 0.8248 | 0.6594 | 0.6823 | 0.6707 | 0.7457 |
| 1.1706 | 12.0 | 852 | 0.7988 | 0.6610 | 0.7189 | 0.6888 | 0.7654 |
| 1.1706 | 13.0 | 923 | 0.8593 | 0.6587 | 0.6762 | 0.6673 | 0.7423 |
| 1.1706 | 14.0 | 994 | 0.8204 | 0.6719 | 0.6965 | 0.6840 | 0.7534 |
| 0.4317 | 15.0 | 1065 | 0.8478 | 0.6770 | 0.7128 | 0.6944 | 0.7526 |
| 0.4317 | 16.0 | 1136 | 0.8855 | 0.6610 | 0.7149 | 0.6869 | 0.7730 |
| 0.4317 | 17.0 | 1207 | 0.9091 | 0.6751 | 0.7067 | 0.6905 | 0.7560 |
| 0.4317 | 18.0 | 1278 | 0.9201 | 0.6555 | 0.7169 | 0.6848 | 0.7568 |
| 0.4317 | 19.0 | 1349 | 0.9840 | 0.6623 | 0.7189 | 0.6895 | 0.7483 |
| 0.4317 | 20.0 | 1420 | 0.9817 | 0.6833 | 0.7251 | 0.7036 | 0.7543 |
| 0.4317 | 21.0 | 1491 | 0.9958 | 0.6583 | 0.6945 | 0.6759 | 0.7509 |
| 0.2121 | 22.0 | 1562 | 0.9340 | 0.6647 | 0.7026 | 0.6832 | 0.7722 |
| 0.2121 | 23.0 | 1633 | 0.9906 | 0.6622 | 0.7108 | 0.6857 | 0.7619 |
| 0.2121 | 24.0 | 1704 | 1.0099 | 0.6692 | 0.7088 | 0.6884 | 0.7526 |
| 0.2121 | 25.0 | 1775 | 1.0627 | 0.6673 | 0.7189 | 0.6922 | 0.7662 |
| 0.2121 | 26.0 | 1846 | 1.0744 | 0.6584 | 0.7067 | 0.6817 | 0.7637 |
| 0.2121 | 27.0 | 1917 | 1.1328 | 0.6569 | 0.6864 | 0.6713 | 0.7389 |
| 0.2121 | 28.0 | 1988 | 1.0799 | 0.6641 | 0.7128 | 0.6876 | 0.7577 |
| 0.1201 | 29.0 | 2059 | 1.1156 | 0.6628 | 0.7047 | 0.6831 | 0.7568 |
| 0.1201 | 30.0 | 2130 | 1.0839 | 0.6628 | 0.6965 | 0.6792 | 0.75 |
| 0.1201 | 31.0 | 2201 | 1.1511 | 0.6526 | 0.6925 | 0.6719 | 0.7389 |
| 0.1201 | 32.0 | 2272 | 1.1140 | 0.6737 | 0.7149 | 0.6937 | 0.7543 |
| 0.1201 | 33.0 | 2343 | 1.1094 | 0.6609 | 0.6986 | 0.6792 | 0.7466 |
| 0.1201 | 34.0 | 2414 | 1.1332 | 0.6755 | 0.7251 | 0.6994 | 0.7534 |
| 0.1201 | 35.0 | 2485 | 1.1322 | 0.6841 | 0.7189 | 0.7011 | 0.7551 |
| 0.0776 | 36.0 | 2556 | 1.1603 | 0.6711 | 0.7189 | 0.6942 | 0.7551 |
| 0.0776 | 37.0 | 2627 | 1.1460 | 0.6504 | 0.7047 | 0.6764 | 0.7543 |
| 0.0776 | 38.0 | 2698 | 1.1387 | 0.6584 | 0.7067 | 0.6817 | 0.7577 |
| 0.0776 | 39.0 | 2769 | 1.1438 | 0.6641 | 0.7088 | 0.6857 | 0.7534 |
| 0.0776 | 40.0 | 2840 | 1.1791 | 0.6660 | 0.7149 | 0.6896 | 0.7577 |
| 0.0776 | 41.0 | 2911 | 1.1701 | 0.6641 | 0.7088 | 0.6857 | 0.75 |
| 0.0776 | 42.0 | 2982 | 1.1889 | 0.6615 | 0.6965 | 0.6786 | 0.7457 |
| 0.0571 | 43.0 | 3053 | 1.1810 | 0.6533 | 0.6945 | 0.6732 | 0.7449 |
| 0.0571 | 44.0 | 3124 | 1.1944 | 0.6577 | 0.6965 | 0.6766 | 0.7440 |
| 0.0571 | 45.0 | 3195 | 1.2032 | 0.6564 | 0.6925 | 0.6739 | 0.7432 |
| 0.0571 | 46.0 | 3266 | 1.2092 | 0.6609 | 0.6945 | 0.6773 | 0.7449 |
| 0.0571 | 47.0 | 3337 | 1.1864 | 0.6622 | 0.6986 | 0.6799 | 0.7466 |
| 0.0571 | 48.0 | 3408 | 1.1972 | 0.6538 | 0.6925 | 0.6726 | 0.7449 |
| 0.0571 | 49.0 | 3479 | 1.1899 | 0.6545 | 0.6945 | 0.6739 | 0.7449 |
| 0.0467 | 50.0 | 3550 | 1.1905 | 0.6552 | 0.6965 | 0.6752 | 0.7449 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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